Myocardial Deformation in the Systemic Right Ventricle: Strain Imaging Improves Prediction of the Failing Heart

Abstract

Background: Predicting heart failure events in patients with a systemic right ventricle (sRV) due to transposition of the great arteries (TGA) is important for timely intensification of follow-up. This study assessed the value of strain compared with currently used parameters as predictor for heart failure–free survival in patients with sRV. Methods: In participants of a multicentre trial, speckle-tracking echocardiography (STE) was performed to assess global longitudinal strain (GLS), mechanical dispersion (MD), and postsystolic shortening (PSS). Cox regression was used to determine the association of STE parameters with the combined end point of progression of heart failure and death, compared with cardiovascular magnetic resonance (CMR) and computed tomography (CT) derived parameters. Results: Echocardiograms of 60 patients were analyzed (mean age 34 ± 11 years, 65% male, 35% congenitally corrected TGA). Mean GLS was −13.5 ± 2.9%, median MD was 49 (interquartile range [IQR] 30-76) ms, and 14 patients (23%) had PSS. During a median 8 (IQR 7-9) years, 15 patients (25%) met the end point. GLS, MD, and PSS were all associated with heart failure–free survival in univariable analysis. After correction for age, only GLS (optimal cutoff > −10.5%) and CMR/CT-derived sRV ejection fraction (optimal cutoff < 30%) remained associated with heart failure–free survival: hazard ratio (HR) 8.27, 95% confidence interval (CI) 2.50-27.41 (P < 0.001), and HR 4.34, 95% CI 1.48-12.74 (P = 0.007), respectively). Combining GLS and ejection fraction improved prediction, with patients with both GLS > −10.5% and sRV ejection fraction < 30% at highest risk (HR 19.69, 95% CI 4.90-79.13; P < 0.001). Conclusions: The predictive value of GLS was similar to that of CMR/CT-derived ejection fraction. The combination of GLS and ejection fraction identified patients at highest risk of heart failure and death. Easily available STE parameters can be used to guide follow-up intensity and can be integrated into future risk prediction scores.